Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://github.com/bridgeqiqi/DREAM.

翻译：大多数传统的人群计数方法采用全监督学习框架，建立场景图像与人群密度图之间的映射关系。这些方法通常依赖大量成本高昂且耗时的像素级标注作为训练监督。缓解标注压力并提高计数准确性的途径之一是利用大量未标注图像，这得益于单幅图像内在的自结构信息与排序一致性，可为训练提供额外的定性关系监督。与早期在原始图像层面利用排序关系的方法不同，我们探索了潜在特征空间中的这种排序一致性关系。该方法能够融合大量金字塔偏序结构，增强模型表示能力。其显著优势在于还能提高未标注样本的利用率。具体而言，我们提出了深度排序一致性金字塔模型（DREAM），该模型充分利用潜在空间中从粗到细金字塔特征的排序一致性，借助海量未标注图像实现增强的人群计数。此外，我们收集了包含4000张图像的新未标注人群计数数据集FUDAN-UCC用于训练。在UCF-QNRF、上海科技大学PartA和PartB及UCF-CC-50四个基准数据集上的广泛实验表明，与现有半监督方法相比，我们的方法具有有效性。相关代码开源于https://github.com/bridgeqiqi/DREAM。